Digital histopathology slides have many sources of variance, and while pathologists typically do not struggle with them, computer aided diagnostic algorithms can perform erratically. This manuscript presents Stain Normalization using Sparse AutoEncoders (StaNoSA) for use in standardizing the color distributions of a test image to that of a single template image. We show how sparse autoencoders can be leveraged to partition images into tissue sub-types, so that color standardization for each can be performed independently. StaNoSA was validated on three experiments and compared against five other color standardization approaches and shown to have either comparable or superior results.
Variational autoencoders (VAEs) employ Bayesian inference to interpret sensory inputs, mirroring processes that occur in primate vision across both ventral (Higgins et al., 2021) and dorsal (Vafaii et al., 2023) pathways.
Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, College of Chemistry, Nankai University, Tianjin 300071, China.
Due to the complexity of samples and the limitations in spatial resolution, the spectra in hyperspectral imaging (HSI) are generally contributed to by multiple components, making univariate analysis ineffective. Although feature extraction methods have been applied, the chemical meaning of the compressed variables is difficult to interpret, limiting their further applications. An unmixing autoencoder (UAE) was developed in this work for the separation of the mixed spectra in HSI.
Genomic selection (GS) in aquatic animal breeding is gaining traction for its improved accuracy and speed over traditional pedigree methods, with artificial intelligence (AI) playing a key role.
Deep learning (DL) techniques, such as deep neural networks (DNNs) and convolutional neural networks (CNNs), are being utilized to enhance phenotyping, genotyping, and genomic estimated breeding value (GEBV) predictions.
The article suggests that as DL technology advances, it will likely be applied to a wider range of aquaculture species in molecular breeding efforts.
Single-cell RNA sequencing (scRNA-seq), a powerful technique for investigating the transcriptome of individual cells, enables the discovery of heterogeneous cell populations, rare cell types, and transcriptional dynamics in separate cells. Yet, scRNA-seq data analysis is limited by the problem of measurement dropouts, i.e.
Distributed denial of service (DDoS) attack is one of the most hazardous assaults in cloud computing or networking. By depleting resources, this attack renders the services unavailable to end users and leads to significant financial and reputational damage. Hence, identifying such threats is crucial to minimize revenue loss, market share, and productivity loss and enhance the brand reputation.
